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Feature Freshness Budgets for Real-Time ML Inference Under Stream Lag

Amit Rajula

arXiv:2610.02259v1cs.DCcs.DB

TL;DR

Online feature stores create a freshness gap that is not formally quantified as a bounded, cost-quantified quantity. The paper defines per-feature freshness budgets and proves staleness and permanent-drop results; the threshold reproduces in simulation and real infrastructure, while a sampling-phase artifact explains the principal simulation-to-infrastructure gap.

  • Problem

    Existing treatments name the freshness gap but do not quantify it as a bounded, cost-quantified quantity, leaving the tolerated staleness and cost of exceeding it unformalized.

    Source: §1 · pp. 1–2

  • Method

    The paper models an online feature store as a materialized view over an event stream, defines per-feature freshness budgets, and proves staleness-bound and permanent-drop propositions.

    Source: §1 · p. 2

  • Results

    The closed-form collapse threshold reproduces in simulation and a Kafka–Redis–PostgreSQL pipeline, while the principal simulation-to-infrastructure gap is a sampling-phase artifact rather than latency.

    Source: §9 · p. 9

  • Takeaways & Limitations

    Feature freshness is a bounded resource to measure, budget, and deliberately spend when it runs out.

    Source: §9 · p. 9

    The workload is synthetic and single-entity, and the fixed-threshold decision rule isolates staleness but does not establish error rates for any particular trained model.

    Source: §8 · p. 9

Abstract

from arXiv · show

Online feature stores decouple feature computation from model serving, materializing features from upstream event streams into a low-latency store that inference reads at request time. This decoupling introduces a freshness gap - the interval between an event occurring at the source and its effect becoming visible in the served feature vector - that is widely documented as a cause of training-serving skew but has no formal treatment as a bounded, cost-quantified quantity. We model the online feature store as a materialized view over an event stream and define a per-feature freshness budget: the difference between the decision window a feature is consumed within and the staleness the serving pipeline imposes on it. We prove two propositions - a per-feature staleness bound, and a closed-form threshold below which a feature can never be served within budget - and validate them in a seeded, reproducible discrete-event simulation and on a real Apache Kafka, Redis, and PostgreSQL pipeline. The predicted collapse threshold reproduces on real infrastructure, with the decision-error transition landing where the closed form predicts. Reconciling simulated and real drop rates surfaces a methodological result of independent interest: the dominant simulation-to-infrastructure gap is not latency but a sampling-phase artifact - a simulation whose request clock and recomputation cadence are phase-locked systematically under-observes staleness. Randomizing that phase reconciles simulation and infrastructure to within 0.018 mean absolute error in per-feature drop rate. Results are scoped to the studied workloads and configurations and are not claims about any specific production feature-store implementation.

1 Introduction

Online feature stores decouple feature computation from low-latency model serving, but this creates a freshness gap that existing engineering treatments do not quantify. The paper defines per-feature budgets, proves staleness and permanent-drop results, validates them in simulation and on real infrastructure, and identifies a sampling-phase artifact; its quantitative claims are scoped to the studied workloads and configurations.

1 Introduction

  • The freshness gap between a source event and its appearance in the served feature vector is named in the literature but not quantified.The paper connects excessive staleness to training–serving skew, silent prediction degradation, and possible monetary loss.Source: §1 · p. 1
  • The paper replaces the binary fresh-or-broken framing with a quantitative account of tolerated staleness and the cost of serving beyond that tolerance.Existing engineering treatments ask whether a feature is fresh enough, rather than how much staleness a decision can tolerate or what exceeding that tolerance costs.Source: §1 · p. 2
  • The per-feature freshness budget separates a task’s decision window from the staleness imposed by the serving pipeline.The online feature store is modeled as a materialized view over an upstream event stream.Source: §1 · p. 2
  • The paper proves a per-feature staleness bound and a closed-form permanent-drop threshold determined by pipeline parameters, independent of fallback policy.Below the threshold, a feature can never be served within budget.Source: §1 · p. 2
  • The propositions are validated in a seeded, reproducible simulation and a real Apache Kafka, Redis, and PostgreSQL pipeline, where the predicted collapse threshold reproduces.The validation covers both simulated and real serving infrastructure.Source: §1 · p. 2
  • Phase-locked simulations systematically under-observe staleness; randomizing sampling phase reconciles simulated and real drop rates to within 0.018 mean absolute error.The paper identifies this sampling-phase artifact as a methodological requirement for freshness simulation.Source: §1 · p. 2
  • Quantitative results characterize the studied synthetic workloads and pipeline configurations, not any specific production feature store.The decision rule is a fixed threshold rather than a trained model, isolating staleness as the independent variable.Source: §1 · p. 2

2 Background and Related Work

The paper positions per-feature freshness budgeting as complementary to existing correctness mechanisms for feature stores and related work on freshness and Age of Information. Its focus is residual online staleness, its cost, and when tolerating it helps or fails.

2.1 Feature stores and training–serving skew

  • Point-in-time-correct joins and shared feature-computation logic address sources of training–serving skew, but do not quantify residual staleness once applied.This work instead asks how stale the online value is, what it costs, and when a budgeted policy helps or fails.Source: §2.1 · p. 2

2.2 Freshness, staleness, and materialized views

  • The paper adopts the materialized-view framing directly, modeling an online feature as a view over an event stream, following work that trades refresh cost against view staleness.Prior work on replica staleness gives expected bounds on how far a replica trails its source under quorum replication.Source: §2.2 · p. 2
  • Windowed streaming workloads can concentrate delay in waiting for a window to close rather than computation, motivating per-feature budgets and a provable collapse threshold.The paper’s contribution is to quantify the gap as a budget and test it on real serving infrastructure.Source: §2.2 · p. 3

2.3 Age of Information

  • The paper shares Age of Information’s cost-oriented treatment of freshness, but applies it to windowed feature aggregates consumed by a decision rule with its own tolerance.Age of Information is the elapsed time since the freshest received update was generated, and prior work optimizes sampling and transmission against functions of age.Source: §2.3 · p. 3

3 System Model and the Freshness Budget

The model compares a zero-lag oracle with a served feature derived from a single entity’s event stream. It defines freshness budget as task tolerance less served age, then contrasts baseline, blocking, and per-feature fallback policies.

3 System Model and the Freshness Budget

  • The model considers a single entity producing an event stream from which windowed aggregate features, such as trailing-window event counts, are derived.Each feature has an oracle view and a served view.Source: §3 · p. 3
  • The oracle is the feature’s true current value computed with zero lag from the complete event log, serving as ground truth for decisions.It represents what a perfect, instantaneous pipeline would serve.Source: §3 · p. 3
  • The served value is recomputed on the aggregation-window cadence, reflects events only through a watermark trailing by ingestion lag, and incurs read scheduling delay.Served age is measured from the value’s watermark at request time.Source: §3 · p. 3
  • The decision window expresses task tolerance, whereas the aggregation window belongs to the pipeline; freshness budget relates that tolerance to imposed staleness.The two windows must not be conflated.Source: §3 · p. 3
  • A feature is in budget when its freshness budget is nonnegative; otherwise, the budget is exhausted and the feature is over budget.The paper treats freshness as a bounded, tunable resource spent by staleness.Source: §3 · p. 3
  • The baseline serves stored values unchecked, blocking waits for every feature or times out, and budgeted serving drops over-budget features for their last in-budget values.The distinction between blocking and per-feature dropping matters when requests use features with heterogeneous lag.Source: §3 · p. 3

4 Theoretical Results

The theoretical results bound served age by pipeline latency terms and derive when a feature can never meet its decision window. The threshold gives a feature-specific permanent-drop condition independent of fallback strategy.

4 Theoretical Results

  • Served age is bounded by ingestion lag, aggregation window, and scheduling delay, and the propositions are asserted on every read in every simulation run.The proof bounds age using the watermark’s maximum lag and time since recomputation, plus read delay.Source: §4 · p. 4
  • Under uniform budgeting, a feature is permanently dropped when its multiplier falls below a closed-form threshold based on its pipeline parameters.The minimum achievable served age is ingestion lag plus scheduling delay, reached immediately after recomputation; the threshold is independent of fallback strategy.Source: §4 · p. 4

5 Simulation Study

The simulation tests freshness-budget policies across heterogeneous features, finding a sharp T2 collapse at the predicted threshold, while T1 degrades gradually. Fallbacks do not change the threshold, and queueing load can push a previously safe budget into collapse.

5.1 Harness

  • The harness uses a seeded, deterministic event simulation with five heterogeneous features and a uniform multiplier m scaling each feature’s decision window.It compares policy decisions against an exact, zero-lag oracle; all figures are means over multiple seeds.Source: §5.1 · p. 4

5.2 The collapse threshold reproduces the closed form

  • 92.2% T2 decision error at m ≤ 0.50 falls to 0.4% between m = 0.50 and m = 0.51, matching the predicted smallest threshold of 0.5012.Across the broader sweep, T1 latency grows smoothly as the budget tightens while its error never exceeds 2.4%; T2 reaches 92% error below its threshold.Source: §5.2 · p. 5, Table 1 · p. 5

5.3 Fallback strategy affects error, not the threshold

  • Dropped-feature counts are identical across carry-forward, exponential-decay, and linear-extrapolation fallbacks, because the threshold is a pipeline property.For monotonic count features, decay performs slightly worse than carry-forward above threshold by introducing false negatives.Source: §5.3 · p. 5

5.4 Load-induced collapse

  • At utilization above one, T2 error rises from 0.4% to 92% and mean dropped features from 2.4 to 5.0, while T1 degrades gracefully.The collapse occurs with the budget multiplier held at m = 0.70, previously safe under light load; queueing delay adds to effective staleness.Source: §5.4 · p. 5

6 Real-Infrastructure Validation

The authors validate the T2 threshold on a Kafka, Redis, and PostgreSQL pipeline, where the error transition matches Proposition 2. Proposition 1 mostly holds, with small, transient excesses during host scheduling stalls.

6 Real-Infrastructure Validation

  • The validation pipeline uses Kafka for events, Redis for served features, and PostgreSQL for the ground-truth event log.A consumer maintains windowed aggregates in Redis, while the scorer reads served values and queries PostgreSQL for ground truth.Source: §6 · p. 6
  • Real infrastructure exposed failure modes absent from the discrete-event model, including frozen watermarks for quiet features and negative ages from unsynchronized process clocks.The implementation corrected the clock issue by using a shared time origin.Source: §6 · p. 6
  • 35% error below threshold at m = 0.45 falls to zero above m = 0.50, reproducing the predicted collapse on real infrastructure.Table 2 reports T2 decision-error rates and mean dropped features across four multipliers spanning the predicted threshold.Source: §6 · pp. 6–7, Table 2 · p. 7
  • Proposition 1 held on most reads; violations followed a host scheduling stall, with median excess 0.25 s and mean excess 0.32 s.The authors attribute these bounded, transient excesses to poll granularity and network round-trip, and suggest empirical latency slack for deployment planning.Source: §6 · p. 7

7 Reconciling Simulation and Infrastructure

Latency models alone do not explain the simulation–infrastructure drop-rate gap: phase-locked sampling misses staleness. Randomizing the sampling phase reconciles the rates and reproduces the short-window feature’s measured drop rate.

7 Reconciling Simulation and Infrastructure

  • Adding 0.3 s mean latency reduced aggregate drop-rate error from 0.112 to 0.063 but left the one-minute feature at m = 0.70 at zero.A heavy-tailed latency model moved that residual only from 0.000 to 0.086, still below the observed 0.598.Source: §7 · p. 7
  • Phase-locked one-second request and recomputation clocks sample immediately after recomputation, fixing feature age at its cycle floor and missing the upper half of the range.Independent infrastructure processes sample the full recomputation cycle, with age ranging from Lf to Lf + Wf.Source: §7 · p. 7
  • 0.018 mean absolute error in per-feature drop rate across features and multipliers reconciles the random-phase simulation with infrastructure.For the one-minute feature at m = 0.70, the random-phase model predicts 0.599 against a measured 0.598; Table 3 reports model progression and Fig. 2 visualizes agreement and the residual.Source: Table 3 · p. 8, §7 · p. 8, Figure 2 · p. 8
  • The dominant simulation-to-infrastructure discrepancy is sampling phase, so freshness simulations must randomize phase between sampling and recomputation to avoid under-observing staleness.A small mean-latency term accounts for the minor remainder.Source: §7 · p. 8

8 Discussion and Limitations

The discussion presents per-feature bounds and a closed-form collapse threshold, while emphasizing that workload and model choices constrain the quantitative claims.

8 Discussion and Limitations

  • A per-feature staleness bound and closed-form threshold identify when per-feature budgeting is guaranteed to fail.The threshold is reached through tighter budgets or when shared-pipeline utilization crosses one.Source: §8 · pp. 8–9
  • Set freshness budgets using measured end-to-end latency and observed sampling phase, not the nominal pipeline bound alone.Both effects push real drop rates above the zero-latency prediction.Source: §8 · p. 9
  • Quantitative results use synthetic, single-entity workloads; multi-entity contention is represented only through the queueing layer.The fixed-threshold decision rule isolates staleness, so reported error rates concern decision correctness under a known rule, not any particular trained model’s accuracy.Source: §8 · p. 9
  • Simple fallback strategies and configuration-specific results limit direct generalization to production benchmarks.The phase model is not an independent measurement; logging read-to-recomputation phase directly would test the explanation more directly.Source: §8 · p. 9

9 Conclusion

The conclusion frames feature-store staleness as a budgeted resource with a provable collapse threshold validated on real infrastructure. It identifies sampling phase, rather than latency, as the principal simulation gap.

9 Conclusion

  • A closed-form freshness-budget collapse threshold reproduces in simulation and a Kafka–Redis–PostgreSQL pipeline, and load can reach it as well as policy.The conclusion reframes feature freshness as something to measure, budget, and deliberately spend when it runs out.Source: §9 · p. 9
  • The principal simulation-to-infrastructure gap is a sampling-phase artifact rather than latency, constraining how freshness simulations must be built.The paper’s conclusion characterizes feature freshness as a bounded resource, not a property to perfect or a failure to prevent.Source: §9 · p. 9
  • The budget structure was validated on real serving infrastructure.The conclusion presents staleness as a resource to measure, budget, and deliberately spend when exhausted.Source: §9 · p. 9

Data and Code Availability

The simulation harness, real-infrastructure pipeline, and analysis scripts are seeded and deterministic, and scripts regenerate every reported table.

Data and Code Availability

  • Seeded, deterministic scripts regenerate every reported table.The available materials include the simulation harness, real-infrastructure pipeline, and analysis scripts.Source: Data and Code Availability · p. 9
  • The real-infrastructure pipeline uses Docker Compose with producer, consumer, and scorer processes.The three processes must share a common time origin for real-infrastructure runs.Source: Data and Code Availability · p. 9
  • Each reported figure regenerates from its corresponding script.The harness and analysis are seeded and deterministic.Source: Data and Code Availability · p. 9

Declarations

The author reports no relevant competing interests or study funding and describes the use of an LLM for prose and code assistance, while retaining accountability for the research.

Declarations

  • The author declares no competing interests relevant to the article.Source: Declarations · p. 9
  • No funding was received for conducting the study.Source: Declarations · p. 9
  • An LLM assisted with drafting, editing, and code scaffolding; the author produced and verified the research design, claims, theoretical results, and measurements, and takes full accountability.Source: Declarations · p. 9
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